A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Generative model predicts remaining life of damaged structures.
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce…
The U.S. water distribution system contains thousands of miles of pipes constructed from different materials, and of various sizes, and age. These pipes suffer from physical, environmental, structural and operational stresses, causing deterioration which eventually leads to their failure. Pipe deterioration results in …
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates t…
Graph neural networks improve equipment health monitoring from multisensor data.
Bayesian deep learning improves maintenance planning uncertainty quantification.
Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which lever…
Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent…
This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for mechanical component…
Bounds derived for contract values in life insurance with financial market interaction.
Paper proposes a natural hedging framework with graphical assessment for longevity risk management.
This paper proposes a multi-head attention model for predicting RUL in IIoT environments.
Paper introduces a hybrid GPR model for more interpretable RUL prediction in aeroengine.
With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of those components. In this paper we work with components of backend data servers su…
In the last decade, deep learning (DL) has outperformed model-based and statistical approaches in predicting the remaining useful life (RUL) of machinery in the context of condition-based maintenance. One of the major drawbacks of DL is that it heavily depends on a large amount of labeled data, which are typically expe…
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model…
Mix-up domain adaptation improves dynamic RUL predictions across various conditions.
Proposes ECLSTM for more accurate RUL estimation from time series data.
Paper tackles RUL prediction with scarce data using indirect supervision.
In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply …
This study compares direct and indirect methods for estimating own funds in life insurance, finding indirect methods more effective under realistic asset-liability coupling.
Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is…
We determine how an individual can use life insurance to meet a bequest goal. We assume that the individual's consumption is met by an income, such as a pension, life annuity, or Social Security. Then, we consider the wealth that the individual wants to devote towards heirs (separate from any wealth related to the afor…
The paper revisits and applies FTAP to life insurance and annuities pricing.
Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.
ATS2S model predicts RUL of industrial equipment using attention mechanism.
The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very simi…
GANs can learn hierarchical distributions in real-world images efficiently.
Bayesian MS-VAR model for pricing equity-linked life insurance products.
The study examines how different interpolation methods affect the decomposition of life insurance surplus.
New algorithm forecasts health indicators for better equipment lifespan prediction.
This paper explores how machine learning can improve life insurance risk assessment.
In this paper, we study a stochastic optimal control problem with stochastic volatility. We prove the sufficient and necessary maximum principle for the proposed problem. Then we apply the results to solve an investment, consumption and life insurance problem with stochastic volatility, that is, we consider a wage earn…
Transformers cluster meaningless words around leaders for sentiment analysis.
Optimizes capital structure for life insurance companies with surplus participation.
Investigates optimal life insurance and annuity decisions in inflationary economies.
Two-dimensional transition rates improve life insurance reserve calculations.
The paper adds explanation to predictive process monitoring.
Background: Overweight and obesity are an increasing phenomenon worldwide. Predicting future overweight or obesity early in the childhood reliably could enable a successful intervention by experts. While a lot of research has been done using explanatory modeling methods, capability of machine learning, and predictive m…
We provide a novel approach and an exploratory study for modelling life event choices and occurrence from a probabilistic perspective through causal discovery and survival analysis. Our approach is formulated as a bi-level problem. In the upper level, we build the life events graph, using causal discovery tools. In the…
Study large deviations in life insurance portfolios without identical distributions.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
The paper optimizes investment strategies with constraints for life-cycle models.
LIFE framework improves model accuracy and interpretability.
Reinsurance can help life insurers maintain higher capital guarantees without losing utility.
We consider the problem of how an individual can use term life insurance to maximize the probability of reaching a given bequest goal, an important problem in financial planning. We assume that the individual buys instantaneous term life insurance with a premium payable continuously. By contrast with Bayraktar et al. (…
We use the maximum entropy principle for pricing the non-life insurance and recover the Bühlmann results for the economic premium principle. The concept of economic equilibrium is revised in this respect.